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WO2012085750A1 - Détection d'état de santé de patient et mortalité - Google Patents

Détection d'état de santé de patient et mortalité Download PDF

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Publication number
WO2012085750A1
WO2012085750A1 PCT/IB2011/055610 IB2011055610W WO2012085750A1 WO 2012085750 A1 WO2012085750 A1 WO 2012085750A1 IB 2011055610 W IB2011055610 W IB 2011055610W WO 2012085750 A1 WO2012085750 A1 WO 2012085750A1
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WO
WIPO (PCT)
Prior art keywords
information
patient
icu
state machine
clinical
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Ceased
Application number
PCT/IB2011/055610
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English (en)
Inventor
Nicolas Wadih CHBAT
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Koninklijke Philips NV
Original Assignee
Koninklijke Philips Electronics NV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips Electronics NV filed Critical Koninklijke Philips Electronics NV
Priority to US13/996,565 priority Critical patent/US20130290231A1/en
Priority to RU2013133868/08A priority patent/RU2013133868A/ru
Priority to EP11810654.1A priority patent/EP2656259A1/fr
Publication of WO2012085750A1 publication Critical patent/WO2012085750A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present application finds particular application in medical diagnostic systems, e.g. patient condition diagnosis.
  • the described technique may also find application in other diagnostic systems, other patient modeling scenarios, or other diagnostic techniques.
  • Patient diagnosis is a complex matter that often requires the consideration of several information sources. With advances in computer processing speed and data storage, such information sources have become more readily-available to physicians, but knowing where to look for diagnostic assistance and how to apply medical information once it is located can be a computationally-complex task. Moreover, once a physician has access to relevant diagnostic information from multiple sources, the physician must weigh the different information sources to generate a reliable diagnosis, which further complicates the diagnosis procedure.
  • the present application provides new and improved systems and methods for detecting patient medical conditions, which overcome the above-referenced problems and others.
  • a system that facilitates predicting onset of a medical condition in a patient includes a plurality of medical information databases, and a processor that executes computer-executable instructions that are stored in a memory, the instructions comprising aggregating medical information input from the plurality of information database, and inputting aggregated medical information into each of an inference algorithm, a Bayesian network, and a finite state machine.
  • the instructions further comprise executing each of the inference algorithm, the Bayesian network, and the finite state machine, and aggregating output information from each of the inference algorithm, the Bayesian network, and the finite state machine.
  • the instructions further comprise determining whether a patient has the medical condition based at least in part on the aggregated output information, and controlling a display to display the determination of whether the patient has the medical condition to a user on a display.
  • a method of predicting onset of a medical condition in a patient includes aggregating medical information input from a plurality of information databases, inputting aggregated medical information into each of an inference algorithm, a Bayesian network, and a finite state machine, and executing each of the inference algorithm, the Bayesian network, and the finite state machine.
  • the method further includes aggregating output information from each of the inference algorithm, the Bayesian network, and the finite state machine, determining whether a patient has the medical condition based at least in part on the aggregated output information, and controlling a display to display the determination of whether the patient has the medical condition to a user on a display.
  • a method of predicting whether a patient has a specified medical condition includes aggregating a plurality of medical knowledge sources, inputting clinical knowledge-based rules, pre-intensive care unit (pre-ICU) information, and ICU data into an inference algorithm, inputting clinical research-based probability information, pre-ICU information, and ICU data into a Bayesian network, and inputting clinical definition-based logic flows, pre-ICU information, and ICU data into a state machine.
  • the method further includes aggregating output information from each of the inference algorithm, the Bayesian network, and the state machine to determine whether the patient has the specified medical condition, and outputting the determination of whether the patient has the specified condition to a user.
  • One advantage is that patient condition detection is improved. Another advantage resides in reducing patient mortality rates.
  • FIGURE 1 illustrates a system for detecting medical problems in a patient.
  • FIGURE 2 illustrates a receiver-operator curve (ROC) showing condition onset for a specified condition as determined by the inference algorithm.
  • ROC receiver-operator curve
  • FIGURE 3 illustrates a receiver-operator curve (ROC) showing condition onset for a specified condition as determined by the Bayesian network.
  • ROC receiver-operator curve
  • FIGURE 4 illustrates a receiver-operator curve (ROC) showing condition onset for a specified condition as determined by the state machine.
  • ROC receiver-operator curve
  • FIGURE 5 illustrates a GUI, which is presented to a user on a computer display.
  • FIGURE 6 illustrates a method of aggregating medical information sources as input for a plurality of modeling algorithms, executing the algorithms, and combining the algorithm outputs to determine whether a patient has or will imminently have a specified medical condition.
  • the subject innovation overcomes the problem of poor detection rates by combining multiple sources of knowledge, modeling the knowledge sources into a format that is usable by multiple algorithms, and combining the output of the multiple algorithms to more accurately predict condition onset. For instance, several knowledge sources can be input to each of an inference algorithm, a Bayesian network, and a finite state machine, and the outputs of each algorithm can be combined, optionally weighted, etc., to make a final determination of the likelihood that the patient has or will imminently have a specified condition.
  • FIGURE 1 illustrates a system 100 for detecting medical problems in a patient.
  • the system includes a processor 102 that executes, and a memory 104 that stores, computer-executable instructions (e.g., algorithms, routines, executables, programs, etc.) for carrying out the various protocols, procedures, methods, functions, modules, etc., described herein.
  • the processor 102 and memory 104 are coupled to a user interface 106 that includes an input device into which a user enters information and a display 108 on which information is output or displayed to the user.
  • a plurality of inputs 112 are input into the memory (e.g., via the user interface or downloaded locally or remotely from one or more databases).
  • the inputs 112 are analyzed and/or manipulated by a plurality of algorithms 114 executed and/or maintained by the processor 102 to generate a plurality of outputs 116 that are presented to a user on the display 110.
  • the inputs include three initial sources of knowledge: a clinical knowledge database 118 from which rules are generated by a rules generation module; a clinical research database 122 from which probabilities are generated by a probability generation module 124; and a clinical definitions database 126 that includes published standards from which a logic flow is generated by a logical flow generation module 128.
  • a "module” is a set of computer-executable instructions that are stored on a computer- readable medium, such as the memory 104 for execution by the processor 102 or other means for performing the described function.
  • the rules generated by the rules generation module 120 are used by the processor 102 to configure an inference algorithm 134.
  • the probabilities generated by the probability generation module 124 are used by the processor 102 to configure a Bayesian network 136.
  • the logic flow generated by the logic flow generation module 128 is used by the processor 102 to configure a state machine 138.
  • Pre-ICU data may include without limitation data related to patient demographics, chronic diseases and conditions, and events data.
  • ICU data may include without limitation vital signs and medicines.
  • the pre-ICU data and ICU data are also fed into all three algorithms 134, 136, 138.
  • the outputs of the inference algorithm 134, the Bayesian network 136, and the state machine 138 are subject to a threshold comparison that indicates whether the patient has or will imminently have a specified condition, or the probability that the patient has such a condition. For instance, based on the output of the three algorithms 114, an onset condition probability (e.g., 60% likelihood, 90% likelihood, etc.,) may be presented to the user as an onset output 140.
  • a threshold comparison that indicates whether the patient has or will imminently have a specified condition, or the probability that the patient has such a condition. For instance, based on the output of the three algorithms 114, an onset condition probability (e.g., 60% likelihood, 90% likelihood, etc.,) may be presented to the user as an onset output 140.
  • an onset condition probability e.g., 60% likelihood, 90% likelihood, etc.
  • the onset output 140 is a "yes" or "no,” which is determined as a result of the comparison of a probability determined from the three algorithms 114 to a predetermined threshold (e.g., if the algorithms 114 indicate a greater than 50% change that the patient has the specified condition, then the onset output 140 is a "yes,” and otherwise it is a "no.”
  • the outputs of the state machine 138 include shock and immune system information 142 (e.g., septic shock, hypovolemic shock, cardiogenic shock, whether the immune system has been compromised, etc.).
  • ICU data 132 may also be output directly by the processor 102 as one or more plots or graphs 144 (e.g., vital signs, drug or medicinal dose information, etc.)
  • five main knowledge sources of a condition facilitate the development and execution of three algorithms 114.
  • the condition is detected independently by each of the inference algorithm 134, the Bayesian network 136, and the finite state machine 138.
  • ultimate condition onset determination is performed based on 2 out of 3 algorithms detecting the condition.
  • the different algorithms complement each other in that they account for and use different types of information.
  • the interface algorithm 134 deals with imprecise and/or subjective values (e.g., warm or cool, large or small, etc.), while the Bayesian network deals with discrete values, such as heart rate, respiratory rate, etc.
  • the state machine accounts for logical if-then flows or information, and outputs a status (e.g., yes or no).
  • the system 100 includes the processor 102 that executes, and the memory 104, which stores, computer-executable instructions (e.g., routines, programs, algorithms, software code, etc.) for performing the various functions, methods, procedures, etc., described herein.
  • computer-executable instructions e.g., routines, programs, algorithms, software code, etc.
  • module denotes a set of computer-executable instructions, software code, program, routine, or other means for performing the described function, or the like, as will be understood by those of skill in the art.
  • the memory may be a computer-readable medium on which a control program is stored, such as a disk, hard drive, or the like.
  • a control program stored in any computer-readable medium
  • Common forms of non-transitory computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic storage medium, CD-ROM, DVD, or any other optical medium, RAM, ROM, PROM, EPROM, FLASH-EPROM, variants thereof, other memory chip or cartridge, or any other tangible medium from which the processor can read and execute.
  • the systems described herein may be implemented on or as one or more general purpose computers, special purpose computer(s), a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmable logic device such as a PLD, PLA, FPGA, Graphical card CPU (GPU), or PAL, or the like.
  • the system 100 of Figure 1 is used to generate mortality studies on virtual populations of patients, e.g., past patient records. For instance a number of virtual patients may be generated and input into the system (e.g., using the GUI 230 of Figure 7), and mortality studies can be generated as a function of specific criteria common to a sub-population in the virtual patient population. In this manner, variables that contribute to condition onset are isolated.
  • FIGURE 2 illustrates a receiver-operator curve (ROC) 180 showing condition onset for a specified condition as determined by the inference algorithm 134 (Fig. 1).
  • ROC 180 plotted points form a curve 184 above and left of the line 182 indicate a likelihood or high probability (i.e., greater than 50%) that the patient has or will imminently have the specified condition.
  • a region 186 of the curve 184 includes data points that can be selected as characteristic points for evaluation (e.g., having a relatively high sensitivity value and a relatively low specificity value.
  • FIGURE 3 illustrates a receiver-operator curve (ROC) 190 showing condition onset for a specified condition as determined by the Bayesian network 136 (Fig. 1).
  • ROC receiver-operator curve
  • plotted points form a curve 194 above and left of the line 192 indicate a likelihood or high probability (i.e., greater than 50%) that the patient has or will imminently have the specified condition.
  • a region 196 of the curve 194 includes data points that can be selected as characteristic points for evaluation (e.g., having a relatively high sensitivity value and a relatively low specificity value.
  • FIGURE 4 illustrates a receiver-operator curve (ROC) 200 showing condition onset for a specified condition as determined by the state machine 138 (Fig. 1).
  • ROC receiver-operator curve
  • the plot point 204 above and left of the line 202 indicates a likelihood or high probability (i.e., greater than 50%) that the patient has or will imminently have the specified condition.
  • the state machine thus outputs a single yes or no describing the state of the patient based on the input information received.
  • FIGURE 5 illustrates a GUI 230, which is presented to a user on a computer display, such as the display 110 of Figure 1.
  • the GUI 230 is used in, or in place of, the user interface 106 of Figure 1.
  • the GUI 230 facilitates entering chronic patient information and for running what-if scenarios, similar to those used in order to generate the virtual populations described with regard to Figures 5 and 6.
  • the GUI 230 includes a patient data set field 231 allows a user to select a data set for review.
  • the GUI also includes a patient information field 232 into which a user enters patient ID information (e.g., number, name, etc.), and message field 234 into which a user enters a message or via which a message is presented to the user.
  • patient ID information e.g., number, name, etc.
  • a time range field 236 permits a user to select a time range for which patient records are returned for review.
  • a "next" button or icon 238 permits a user to navigate to a subsequent GUI page, when selected.
  • An "ICU” button or icon 240 permits the user to navigate to an ICU page, when selected.
  • a “clear” button or icon 241 permits a user to clear all fields in the GUI 230, when selected.
  • a "chronic health” field 242 comprises a plurality of fields and boxes that may be selected to indicate patient conditions. Additionally, a "current health” field 244 includes a plurality of fields and boxes that may be selected by the user to enter current patient health information.
  • FIGURE 6 illustrates a method related to aggregating medical information from a plurality of sources, inputting the aggregated information into a multi-algorithm model, and determining that a patient has a specified condition based on the model output.
  • FIGURE 8 relates to a series of acts, it will be understood that not all acts may be required to achieve the described goals and/or outcomes, and that some acts may, in accordance with certain aspects, be performed in an order different that the specific orders described.
  • medical knowledge sources are aggregated for inputting into a plurality of algorithms or modules. For instance, clinical knowledge collected from discussions with physicians, experts, or the like, is modeled into a plurality of rules. Clinical research information is manipulated to generate probability tables that correlate patient symptoms and/or signs to a probability that the patient has a given condition. Clinical definition information (e.g., published standards, etc.) are modeled into logical flows that describe patient conditions). Additionally, ICU and pre-ICU information is prepared as input to the plurality of algorithms or modules.
  • the modeled rules, pre-ICU data (e.g., patient demographics, chronic diseases, conditions, events, etc.), and ICU data (e.g., vital sign data, drug administration data, etc.) are input to the inference algorithm 134 to determine whether the patient has the specified condition.
  • the probability information, pre-ICU data (e.g., patient demographics, chronic diseases, conditions, events, etc.), and ICU data (e.g., vital sign data, drug administration data, etc.) are input to the Bayesian network 136 to determine whether the patient has the specified condition.
  • pre-ICU data e.g., patient demographics, chronic diseases, conditions, events, etc.
  • ICU data e.g., vital sign data, drug administration data, etc.
  • output results from the inference algorithm, the Bayesian network, and the state machine are aggregated.
  • the output information is used to generate a virtual patient population that is used to generate mortality rates due to one or more variables associate with the specified medical condition.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Biomedical Technology (AREA)
  • Public Health (AREA)
  • Pathology (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

Selon l'invention, lors d'une prédiction du début d'un état pathologique pour un patient, de multiples sources de connaissances (112) sont agrégées et modélisées en un format qui est utilisable par de multiples algorithmes comprenant un algorithme d'inférence (134), un réseau bayésien (136) et un automate fini (138). Les sorties (116) des multiples algorithmes sont ensuite combinées afin de prédire plus précisément le début de l'état pathologique. Par exemple, plusieurs sources de connaissances peuvent être introduites dans chacun de l'algorithme d'inférence, du réseau bayésien et d'automate fini, et les sorties de chaque algorithme sont combinées, facultativement pondérées, etc., afin d'effectuer une détermination finale de la vraisemblance du fait que le patient souffre ou puisse souffrir d'une manière imminente de l'état pathologique spécifié.
PCT/IB2011/055610 2010-12-21 2011-12-12 Détection d'état de santé de patient et mortalité Ceased WO2012085750A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US13/996,565 US20130290231A1 (en) 2010-12-21 2011-12-12 Patient condition detection and mortality
RU2013133868/08A RU2013133868A (ru) 2010-12-21 2011-12-12 Определение состояния пациента и смертность
EP11810654.1A EP2656259A1 (fr) 2010-12-21 2011-12-12 Détection d'état de santé de patient et mortalité

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US201061425388P 2010-12-21 2010-12-21
US61/425,388 2010-12-21

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Cited By (4)

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Publication number Priority date Publication date Assignee Title
WO2014087296A1 (fr) * 2012-12-03 2014-06-12 Koninklijke Philips N.V. Système et procédé permettant d'optimiser la fréquence de collecte des données et des seuils pour l'algorithme de détection de dégradation
WO2017029396A1 (fr) * 2015-08-20 2017-02-23 Osypka Medical Gmbh Système et procédé de détermination de probabilité de choc
WO2017055949A1 (fr) 2015-09-28 2017-04-06 Koninklijke Philips N.V. Assistance de décision clinique pour diagnostic différentiel d'œdème pulmonaire chez des patients dans un état critique
US10639100B2 (en) 2017-02-10 2020-05-05 St. Jude Medical, Cardiology Division, Inc. Determining ablation location using probabilistic decision-making

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WO2014071145A1 (fr) 2012-11-02 2014-05-08 The University Of Chicago Évaluation du risque pour un patient
US11177022B2 (en) 2016-10-17 2021-11-16 International Business Machines Corporation Workflow for automatic measurement of doppler pipeline
US11195600B2 (en) 2016-10-17 2021-12-07 International Business Machines Corporation Automatic discrepancy detection in medical data
US11276496B2 (en) 2018-11-21 2022-03-15 General Electric Company Method and systems for a healthcare provider assistance system
CN110008350A (zh) * 2019-03-06 2019-07-12 杭州哲达科技股份有限公司 一种基于贝叶斯推理的机泵安康知识库查找方法

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US20100057651A1 (en) * 2008-09-03 2010-03-04 Siemens Medicals Solutions USA, Inc. Knowledge-Based Interpretable Predictive Model for Survival Analysis
US7801591B1 (en) * 2000-05-30 2010-09-21 Vladimir Shusterman Digital healthcare information management

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AU2007310958A1 (en) * 2006-10-19 2008-04-24 Entelos, Inc. Method and apparatus for modeling atherosclerosis

Patent Citations (2)

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US7801591B1 (en) * 2000-05-30 2010-09-21 Vladimir Shusterman Digital healthcare information management
US20100057651A1 (en) * 2008-09-03 2010-03-04 Siemens Medicals Solutions USA, Inc. Knowledge-Based Interpretable Predictive Model for Survival Analysis

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014087296A1 (fr) * 2012-12-03 2014-06-12 Koninklijke Philips N.V. Système et procédé permettant d'optimiser la fréquence de collecte des données et des seuils pour l'algorithme de détection de dégradation
US10039451B2 (en) 2012-12-03 2018-08-07 Koninklijke Philips N.V. System and method for optimizing the frequency of data collection and thresholds for deterioration detection algorithm
WO2017029396A1 (fr) * 2015-08-20 2017-02-23 Osypka Medical Gmbh Système et procédé de détermination de probabilité de choc
US10349901B2 (en) 2015-08-20 2019-07-16 Osypka Medical Gmbh Shock probability determination system and method
WO2017055949A1 (fr) 2015-09-28 2017-04-06 Koninklijke Philips N.V. Assistance de décision clinique pour diagnostic différentiel d'œdème pulmonaire chez des patients dans un état critique
US10639100B2 (en) 2017-02-10 2020-05-05 St. Jude Medical, Cardiology Division, Inc. Determining ablation location using probabilistic decision-making

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EP2656259A1 (fr) 2013-10-30
US20130290231A1 (en) 2013-10-31
RU2013133868A (ru) 2015-01-27

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